Staff Full Stack Development Engineer

Responsibilities:
Operate as either a Staff Full Stack Development Engineer (Individual Contributor) or an Engagement Lead (Management Track).
Own and deliver complex technical initiatives across multiple engagements or strategic programmes.
Lead the design and implementation of enterprise-scale architectures and modernisation initiatives.
Solve complex engineering challenges, including agentic AI architectures, evaluation frameworks, and COBOL-to-Java modernisation.
Define and drive cross-program technical strategy, reference architectures, and engineering standards.
Collaborate with enterprise architecture teams to align target-state solutions with business objectives.
Design scalable, resilient, and high-performance enterprise integration solutions.
Lead cloud transformation initiatives, including PCF-to-GCP migrations and IBM MQ-to-Kafka modernisation.
Establish and drive AI-Driven Software Development Lifecycle (AIDLC) standards across engagements.
Design and implement agentic AI architectures, small language model (SLM) strategies, and retrieval-augmented generation (RAG) solutions.
Define AI evaluation frameworks and governance standards for enterprise AI applications.
Present AI productivity metrics, ROI, and value realisation frameworks to client engineering leadership.
Serve as a trusted technical advisor to senior client stakeholders and engineering leaders.
Influence engineering practices and architectural decisions across multiple engagements.
Management Responsibilities (Engagement Lead):
Lead multiple workstreams, PODs, or strategic engagements simultaneously.
Manage and mentor a team of 3-6 engineers, fostering technical growth and career development.
Own project planning, staffing, demand forecasting, and resource allocation.
Drive delivery governance, business continuity planning (BCP), and program health.
Monitor project KPIs, delivery metrics, risks, and overall client satisfaction.
Lead estimation, delivery planning, and engineering pyramid optimisation.
Coach engineering managers and technical leads while promoting team health and engagement.
Requirements:
Required Technical Skills: Java and Spring Boot, microservices architecture, GitHub and GitHub Copilot, Maven, PostgreSQL, Docker, Jira and Confluence, Google Cloud Platform (GCP), Google Kubernetes Engine (GKE), Cloud Run, Cloud SQL, Pub/Sub, Apache Kafka, SonarQube, Harness CI/CD, Terraform, Camunda or Appian, Vertex AI, IBM Watsonx for Z (preferred), agentic AI frameworks (agents and skills), Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), enterprise integration patterns, and AI evaluation frameworks.
Leadership and Preferred Qualifications:
Proven experience defining enterprise architecture and technical strategy across multiple programmes.
Strong expertise in cloud-native application modernisation and legacy system transformation.
Experience driving AI adoption and engineering productivity initiatives at scale.
Excellent stakeholder management and executive communication skills.
Demonstrated ability to mentor senior engineers and build high-performing teams.
Strong delivery ownership with a focus on quality, scalability, and business outcomes.
Recognised as a trusted advisor to client engineering leadership with impact beyond a single engagement.

Experience
8-12 yrs